Writer Perspective Distiller

Writer Perspective Distiller — given a writer's fiction works, non-fiction texts, and biographical context, distill her core beliefs, worldview, syntactic discipline, argumentative habits, and blacklist into a callable writing-style skill that lets you (or the AI) enter the writer's mindset during composition. Built-in discipline forbids unilateral AI conclusions: distillation requires two human checkpoints (belief-candidate confirmation after surface reading; blacklist sharpening near completion). 作家风格蒸馏器——给定一位作家的虚构作品、非虚构文本与生平定位，蒸馏出她的核心信念、世界观、句法纪律、论证习惯与黑名单，最终产出一份可调用的写作风格 skill，用于在写作时进入这位作家的状态。内置交互纪律禁止 AI 单方面下定论，蒸馏过程必须包含两次用户校验。

"The voice you hear on first read is usually wrong."

Purpose

Distill a writer's core writing posture into a callable perspective skill. The output is not an imitator — it's a switch that places you (or the AI) into the state the writer occupies when she writes. That state has three components: core beliefs, syntactic discipline, and a blacklist.

Inputs (in order of importance)

Input	Required	Notes
1–3 fiction works	Required	At least one complete. Reveals how she handles "people".
Non-fiction / essays / interviews / correspondence	Critical	Fiction shows the narrator's voice; non-fiction shows the author's analytic voice. They often diverge. Skipping non-fiction almost always produces a wrong distillation.
Biographical context	Important	Generation, geography, education, mobility, who she inherits from, who she draws boundaries against.
User-flagged key passages	Critical	Prevents AI surface-read. Usually a passage the user feels "she is most herself" in.
Use-case scenarios	Medium	Business writing? Academic papers? Fiction narration? Essays? Determines the compression direction.

The first two are non-negotiable. With only fiction, you distill a narrator's voice and risk conflating the author with her characters.

Analysis Flow (six sequential readings)

1. Syntactic layer — vocabulary preferences, sentence-length distribution, rhythm, punctuation habits, register mix (formal / colloquial / academic).
2. Object layer — who does she write about? Whom does she NOT write about? Who is her implicit listener?
3. Attitude layer — her stance toward tragedy, comedy, history, intimacy, failure, time. Inferred from what she does, not from what she says.
4. Historical positioning — whom does she inherit from? Which tradition does she continue? From which surface-similar author does she draw a clear line?
5. Blacklist — what devices would she NEVER use? What emotions would she NEVER write?
6. Core-belief compression — one sentence: the thing in her head while writing that she will not deviate from.

Distillation Discipline (iron rules)

1. Core beliefs must be confirmed by the user

The AI is forbidden from drawing conclusions unilaterally. Flow:

• After the third reading, AI proposes 2–3 candidate beliefs, each with a textual basis and a counter-example.
• User selects / rejects / corrects.
• No proceeding to step 6 without user confirmation.

2. A blacklist is mandatory

Listing only positives produces shallow output. The sharpest understanding lives in counter-examples. A blacklist needs at least 6 entries.

3. Distinguish from neighboring authors

If author A shares a surface style with author B (e.g., "1990s intellectual irony"), explicitly mark where A diverges from B — usually at the belief layer, not the syntactic layer.

4. No copying sentence patterns as "homage"

Borrowing structure is fine (e.g., "open with a generational frame"). Borrowing specific sentences is a plagiarism risk. The distilled perspective must state: Do not reuse her specific sentences; reuse only her method of constructing sentences.

5. Test-driven completion

Before shipping, run a quality gate:

• Take a passage of default-AI prose (a paper, a business document).
• Have the distilled voice rewrite it.
• User reads and judges: "Is this her?" If not, return to step 3 and redo.

6. At least two user checkpoints

• First — after surface reading, present belief candidates.
• Second — after blacklist completion, have the user add what they consider the sharpest counter-example.

A SKILL.md with fewer than two checkpoints does not ship.

Output: SKILL.md Template

text

---
name: <author-slug>-perspective
description: |
  1–3 lines: who the author is; compressed core belief; use case.
  If for personal reference, add "LOCAL ONLY — Do not publish" with privacy notes.
---

# <Author Name> · <Verb-form Naming>

> One-sentence core belief (quote block)

## Core Belief
100–200 words. Three things: what she believes; what she does NOT believe; how the two coexist.

## Style Internals
4–6 belief-level phrases (the underlying tone, not operational rules).

## Syntactic Discipline
6–8 specific, operational do's.

## Argumentative / Rhetorical Habits
4–6 moves specific to this author.

## Black Humor / Rhetorical Rules (if applicable)
2–4 entries.

## Counter-Examples / Avoidance List
6–10 don'ts. **The most important section.**

## Application Flow
3–6 steps for rewriting a passage.

## One-Sentence Compression
The overall judgment that emerges from the whole document.

Common Pitfalls

• Surface read mistaken for deep read — first-impression voice is usually wrong. Human correction is required.
• Cosplaying a similar author — identical surface vocabulary can mask completely opposite beliefs. Distinguish via blacklist.
• Borrowing sentences as homage = plagiarism risk — borrow structure, not sentences. State this explicitly.
• Treating political labels as writing tone — political position ≠ writing posture; the two often diverge.
• Distilling without non-fiction — looking only at fiction conflates the narrator with the author.
• Publishing without user correction — beliefs written unilaterally by AI usually capture an unimportant facet.
• Positive lists without counter-examples — "what she does" is insufficient; "what she refuses" must follow.

Application Flow (when invoked)

1. User provides inputs (per §Inputs).
2. AI runs the first 3 readings, lists belief candidates → first checkpoint.
3. User selects / corrects candidates.
4. AI runs the final 3 readings, builds the blacklist → second checkpoint.
5. User sharpens.
6. AI drafts the SKILL.md → runs the test-driven quality gate.
7. If test passes → output <author-slug>-perspective/SKILL.md.
8. If test fails → return to step 2.

One-Sentence Compression

Distill an author's writing posture into a perspective skill that must pass through two human checkpoints — so the voice rests on her beliefs, not on the AI's guess about her surface sound.

---

中文版 · Chinese Version

「Surface read 第一次读出的声音通常是错的。」

用途

把一位作家的笔法蒸馏成一份可在写作时调用的 perspective skill。蒸馏的产物不是模仿器，是进入她写作时的状态的开关——核心信念 + 句法纪律 + 黑名单。

输入物（按重要性排序）

输入	必需	说明
1–3 部虚构作品	必需	至少一部完整。能看到她处理"人"的方式。
非虚构 / 散文 / 访谈 / 通信	极重要	虚构里看到的是"叙事时的声音"，非虚构里看到的是"分析时的声音"，两者经常偏移。少了非虚构基本必错。
生平定位	重要	世代、地缘、教育、流动史、她传承自谁、跟谁划清界限。
用户人为指认的关键段落	极重要	防止 AI 表面读。这通常是用户读过觉得"她最像她自己"的一段。
用途场景	中等	改写商务稿？学术论文？小说叙事？散文？决定 SKILL.md 的压缩方向。

至少要有前两项。只有虚构没有非虚构的情况下，蒸馏出的 voice 只是"叙事者声音"，会把作家与她笔下的人物混淆。

分析流程（六遍读，必须按顺序）

1. 句法层：词汇偏好、句长分布、节奏、标点习惯、半文白 vs 口语 vs 学术的混合比。
2. 对象层：她写谁？她不写谁？谁是她笔下隐含的"听众"？
3. 态度层：对悲剧 / 喜剧 / 历史 / 亲密 / 失败 / 时代的态度——通过她做什么动作显示，不是通过她说什么。
4. 历史定位：传承自谁？她明显在接续哪一条传统？她跟哪个表面相似的作家划清界限？
5. 黑名单：她绝不会用的手法、绝不会写的情绪。这一步比正面列表重要。
6. 核心信念压缩：一句话——她写作时心里那个不允许她偏离的东西是什么。

蒸馏纪律（铁律，违反会翻车）

1. 核心信念必须由用户确认

AI 不允许单方面下断语。流程：

• AI 在第 3 遍读后，列 2–3 个候选信念，每个配一个文本依据 + 一个反例
• 用户选 / 驳 / 修正
• 没有用户校验前不得进入第 6 步

2. 必须有黑名单

只列正面会写废。最锋利的理解都在反例里。黑名单应该至少有 6 条。

3. 必须区分邻近作家

如果作家 A 与作家 B 共享某种表面风格（例如"九十年代知识分子的反讽"），必须明确指出 A 跟 B 在哪一刀上分开——通常分在信念层而非句法层。

4. 禁止抄句式作"致敬"

借结构可以（例如"用世代框架开头"），借具体句子是查重问题。蒸馏出的 perspective 应明确写："不要复用她的具体句子；只复用她写句子的方法。"

5. 测试驱动

蒸馏完成前，跑一次质量门：

• 找一段 AI 默认体的文字（论文、商务稿都行）
• 让蒸馏出的 voice 重写
• 用户读完判断："这是她吗？" 不像就回到第 3 遍读重做。

6. 至少两次用户校验

• 第 1 次：表面阅读后，提信念候选
• 第 2 次：黑名单完成后，让用户加上他认为最锋利的一条反例

少于两次校验的 SKILL.md 不发布。

输出物：SKILL.md 模板

text

---
name: <author-slug>-perspective
description: |
  1–3 行：作家是谁；核心信念压缩；适用场景。
  如属私人参考，加 "LOCAL ONLY — Do not publish" 并说明隐私边界。
---

# <作家姓名> · <一个动词性命名>

> 一句话核心信念（用引号或 quote 块）

## 核心信念
100–200 字。说清三件事：她相信什么；她不相信什么；这两件如何同时成立。

## 风格内核
4–6 条信念性短语（不操作，是底色）。

## 句法纪律
6–8 条具体可操作的 do's。例：用词偏好、断句习惯、标点纪律、节奏规则。

## 论证习惯 / 修辞习惯
4–6 条该作家特有的论证或修辞 moves。

## 黑色幽默 / 修辞规则（如果有）
2–4 条。

## 反例 / 避免清单
6–10 条 don't's。**最重要的部分**。

## 应用流程
改写一段文字时的 3–6 步操作。

## 一句话压缩
最后一句，从全篇渗出来的总判断。

常见陷阱

• Surface read 误当 deep read：第一次读出的声音通常错。配人为校正才能纠偏。
• 把作家 cosplay 成相似作家：表面词汇相同，骨子里的信念可能完全相反。必须用黑名单分开。
• 抄句式作致敬 = 查重风险：借结构不借句子。蒸馏产物里要明写。
• 把政治标签当写作底色：作家的政治位置 ≠ 她写作时的姿态。两者经常错位。
• 没读非虚构就下定论：只看虚构会把叙事者当作家，必错。
• 没让用户校正就发布：AI 单方面写出的"信念"基本都是表面读。
• 正面列表没配反例：只说"她会做什么"立不住，必须说"她绝不会做什么"。

应用流程（用户调用此 skill 后）

1. 用户提供输入物（按 §输入物 表）
2. AI 跑前 3 遍读，列信念候选 → 第 1 次校验
3. 用户选 / 修正候选
4. AI 跑后 3 遍读，列黑名单 → 第 2 次校验
5. 用户加锋
6. AI 写出 SKILL.md → 跑测试驱动质量门
7. 测试通过 → 输出 <author-slug>-perspective/SKILL.md
8. 测试不通过 → 回到第 2 步重做

一句话压缩

把一位作家的写作底色，蒸馏成一份会被人为校验两次的 perspective skill——以确保 voice 立在她的信念上，而不是 AI 对她表面声音的猜测上。